Citation: Bakheet, S.; Al-Hamadi, A.;
Youssef, R. A Fingerprint-Based
Verification Framework Using Harris
and SURF Feature Detection
Algorithms. Appl. Sci. 2022, 12, 2028.
https://doi.org/10.3390/app12042028
Academic Editor: Andrea Prati
Received: 29 December 2021
Accepted: 4 February 2022
Published: 15 February 2022
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Article
A Fingerprint-Based Verification Framework Using Harris
and SURF Feature Detection Algorithms
Samy Bakheet
1,2,
* , Ayoub Al-Hamadi
2,
* and Rehab Youssef
1
1
Faculty of Computers and Artificial Intelligence, Sohag University, Sohag 82524, Egypt;
rehab.youssef@fci.sohag.edu.eg
2
Institute for Information Technology and Communications (IIKT), Otto-von-Guericke-University Magdeburg,
39106 Magdeburg, Germany
* Correspondence: samy.bakheet@fci.sohag.edu.eg (S.B.); ayoub.al-hamadi@ovgu.de (A.A.-H.)
Abstract:
Amongst all biometric-based personal authentication systems, a fingerprint that gives
each person a unique identity is the most commonly used parameter for personal identification.
In this paper, we present an automatic fingerprint-based authentication framework by means of
fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive
histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with
a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast
of fingerprint images. The fingerprint is then authenticated by picking a small amount of information
from some local interest points called minutiae point features. These features are extracted from the
thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to
render significantly improved detection results. For fingerprint matching, the Euclidean distance
between the corresponding Harris-SURF feature vectors of two feature points is used as a feature
matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called
RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate
false matches and incorrect match points. Quantitative experimental results achieved on FVC2002
DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and
feasibility of the proposed framework in terms of achieving average recognition rates of 95% and
92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively.
Keywords:
fingerprint authentication; FFT; Gabor filter; SURF algorithm; Harris corner detector;
fingerprint matching; RANSAC; FVC2002 and FVC2000 fingerprint databases
1. Introduction
Today, in the world of advanced digital technology, there is an increasing need for
security measures that lead to the development of many biometric-based personal authenti-
cation systems. Biometrics is a unique identification science for humans based on essential
behavioral or physical features. Among all biometrics, the fingerprint is the most commonly
utilized biometric on personal identification systems. Additionally, fingerprint-based au-
thentication is now considered one of the most secure and reliable biometric recognition
techniques. The reason why fingerprint recognition is the most popular and attractive
among biometric-based security systems is due to the unchanged ability and uniqueness of
an individual’s fingerprints throughout their life [
1
]. The fingerprint can be described as
a unique pattern of interleaved valleys and ridges on the finger surface, where a ridge is
expressed as a single curved segment, whereas a valley is defined as the region between
two nearby ridges.
Automated fingerprint recognition systems can be broadly categorized as verification
or identification systems [
2
]. Fingerprint verification is the validation of one person through
his fingerprint. The user presents his/her fingerprint together with his/her identity in-
formation like his/her ID number. The system of verification reclaims the template of
Appl. Sci. 2022, 12, 2028. https://doi.org/10.3390/app12042028 https://www.mdpi.com/journal/applsci
